tag: Yolo

IBM’s Energy-Efficient NorthPole AI Unit
By Barry Pangrle - 28 Sep, 2023 - Comments: 0

At this point it is well known that from an energy efficiency standpoint, the biggest bang for the back is to be found at the highest levels of abstraction. Fitting the right architecture to the task at hand i.e., an application specific architecture, will lead to benefits that are hard or impossible to claw back later in the design and implementation flow.  With the huge increase in the inter... » read more

Object Detection CNN Suitable For Edge Processors With Limited Memory
By Technical Paper Link - 14 Jun, 2023 - Comments: 0

A technical paper titled “TinyissimoYOLO: A Quantized, Low-Memory Footprint, TinyML Object Detection Network for Low Power Microcontrollers” was published by researchers at ETH Zurich. Abstract: "This paper introduces a highly flexible, quantized, memory-efficient, and ultra-lightweight object detection network, called TinyissimoYOLO. It aims to enable object detection on microcontrol... » read more

Issues And Challenges In Super-Resolution Object Detection And Recognition
By Jeremy Roberson - 04 May, 2023 - Comments: 0

If you want high performance AI inference, such as Super-Resolution Object Detection and Recognition, in your SoC the challenge is to find a solution that can meet your needs and constraints. You need inference IP that can run the model you want at high accuracy. You need inference IP that can run the model at the frame rate you want: higher frame rate = lower latency, more time for dec... » read more

Identifying PCB Defects with a Deep Learning Single-Step Detection Model
By Technical Paper Link - 22 Jul, 2022 - Comments: 0

This new technical paper titled "End-to-end deep learning framework for printed circuit board manufacturing defect classification" is from researchers at École de technologie supérieure (ÉTS) in Montreal, Quebec. Abstract "We report a complete deep-learning framework using a single-step object detection model in order to quickly and accurately detect and classify the types of manufacturi... » read more

Evaluation of Thermal Imaging on Embedded GPU Platforms for Application in Vehicular Assistance Systems
By Technical Paper Link - 10 Jan, 2022 - Comments: 0

Abstract "This study is focused on evaluating the real-time performance of thermal object detection for smart and safe vehicular systems by deploying the trained networks on GPU & single-board EDGE-GPU computing platforms for onboard automotive sensor suite testing. A novel large-scale thermal dataset comprising of > 35,000 distinct frames is acquired, processed, and open-sourced in challengin... » read more

Will Markets For ML Models Materialize?
By Bryon Moyer - 02 Dec, 2021 - Comments: 0

Developers are spending increasing amounts of time and effort in creating machine-learning (ML) models for use in a wide variety of applications. While this will continue as the market matures, at some point some of these efforts might be seen as reinventing models over and over. Will developers of successful models ever have a marketplace in which they can sell those models as IP to other d... » read more

11 Ways To Reduce AI Energy Consumption
By Bryon Moyer - 13 May, 2021 - Comments: 1

As the machine-learning industry evolves, the focus has expanded from merely solving the problem to solving the problem better. “Better” often has meant accuracy or speed, but as data-center energy budgets explode and machine learning moves to the edge, energy consumption has taken its place alongside accuracy and speed as a critical issue. There are a number of approaches to neural netw... » read more

Edge-Inference Architectures Proliferate
By Bryon Moyer - 04 Feb, 2021 - Comments: 0

First part of two parts. The second part will dive into basic architectural characteristics. The last year has seen a vast array of announcements of new machine-learning (ML) architectures for edge inference. Unburdened by the need to support training, but tasked with low latency, the devices exhibit extremely varied approaches to ML inference. “Architecture is changing both in the comp... » read more

Inferencing At The Edge
By Ed Sperling - 31 Jul, 2019 - Comments: 0

David Fritz, head of corporate strategic alliances at Mentor, a Siemens Business, shows how to apply YOLO (you only look once) at the edge, allowing automotive companies to move from a GPU to a much more efficient processor. That allows inferencing to move much closer to the sensor, so neural networks can be tailored to the type of data being produced. From there the data can be abstracted and ... » read more

Building An Efficient Inferencing Engine In A Car
By Ed Sperling - 05 Jun, 2019 - Comments: 0

David Fritz, who heads corporate strategic alliances at Mentor, a Siemens Business, talks about how to speed up inferencing by taking the input from sensors and quickly classifying the output, but also doing that with low power. » read more

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